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Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

机译:结合NMR和MS / MS预测的代谢组学中未知代谢物的结构解析

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摘要

We introduce a cheminformatics approach that combines highly selective and orthogonal structure elucidation parameters; accurate mass, MS/MS (MS2), and NMR into a single analysis platform to accurately identify unknown metabolites in untargeted studies. The approach starts with an unknown LC-MS feature, and then combines the experimental MS/MS and NMR information of the unknown to effectively filter out the false positive candidate structures based on their predicted MS/MS and NMR spectra. We demonstrate the approach on a model mixture, and then we identify an uncatalogued secondary metabolite in Arabidopsis thaliana. The NMR/MS2 approach is well suited to the discovery of new metabolites in plant extracts, microbes, soils, dissolved organic matter, food extracts, biofuels, and biomedical samples, facilitating the identification of metabolites that are not present in experimental NMR and MS metabolomics databases.
机译:我们引入了一种化学信息学方法,该方法结合了高度选择性和正交的结构解析参数。将准确的质量,MS / MS(MS 2 )和NMR整合到一个分析平台中,从而可以准确地识别非目标研究中的未知代谢产物。该方法从未知的LC-MS功能开始,然后结合未知物的实验MS / MS和NMR信息,以基于假阳性候选结构的预测MS / MS和NMR光谱有效地滤除它们。我们在模型混合物上演示了该方法,然后在拟南芥中鉴定了未分类的次级代谢产物。 NMR / MS 2 方法非常适合在植物提取物,微生物,土壤,溶解的有机物,食品提取物,生物燃料和生物医学样品中发现新的代谢产物,从而有助于鉴定代谢产物。实验NMR和MS代谢组学数据库中未提供。

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